Biblio

Cyber-security training has evolved into an imperative need, aiming to provide cyber-security professionals with the knowledge and skills required to confront cyber-attacks that are increasing in number and sophistication. Training activities are typically associated with evaluation means, aimed to assess the extent to which the trainee has acquired the knowledge and skills whose development is targeted by the training programme, while cyber-security awareness and skill level evaluation means may be used to support additional security-related aspects of organizations. In this paper, we review trainee performance assessment metrics in cyber-security training, aiming to assist designers of cyber-security training activities to identify the most prominent trainee performance assessment means for their training programmes, while additional research directions involving cyber-security training evaluation metrics are also identified.

Professional training is essential for organizations to successfully defend their assets against cyber-attacks. Successful detection and prevention of security incidents demands that personnel is not just aware about the potential threats, but its security expertise goes far beyond the necessary background knowledge. To fill-in the gap for competent security professionals, platforms offering realistic training environments and scenarios are designed that are referred to as cyber-ranges. Multiple cyber-ranges listed under a common platform can simulate more complex environments, referred as cyber-range federations. Security education approaches often implement gamification mechanics to increase trainees’ engagement and maximize the outcome of the training process. Scoring is an integral part of a gamification scheme, allowing both the trainee and the trainer to monitor the former’s performance and progress. In this article, a novel scoring model is presented that is designed to be agnostic with respect to the source of information: either a CR or a variety of different CRs being part of a federated environment.

Human is the weakest link in information security. Even with strong cyber security policies an organization can still be hacked because of a human error. Even if people are aware of the policies and their importance they might not behave accordingly. This shows to the importance of studying and measuring human behavior toward cyber security policies. This paper introduces a new instrument that can be used to measure human behavior toward cybersecurity policies through creative measures. The goal is to gather data about human behaviors toward cybersecurity policies in natural environment. This method of gathering information allows people to behave normally and don't feel the need to answer perfectly. The paper illustrates all the previous work related to the subject, summarizing previous work in order to improve what have been previously done. The methodology seeks on measuring behavior based on specific measures. These measures are the password, email, identity, sensitive data, and physical/resource security. Each measure has a number of policies used to measure behavior. These policies were selected among several policies based on literature from the same field and the opinion of experts in the field. These question that went through several rounds of check were used to build the proposed-instrument. This instrument then shall be used by researchers to collect data and perform the required analysis. This paper discusses the behavior pattern in a detail and concise manner. The paper demonstrates that it is posable to measure behavior if the right we questions were asked in the right way.

Our prototype app, Pocket Penjing, built using Unity3D, takes its name from the Chinese "Penjing." These tray plantings of miniature trees pre-date bonsai, often including miniature benches or figures to allude to people's relationship to the tree. App users choose a species, then create and name their tree. Swiping rotates a 3D globe showing flagged locations. Each flag represents a live online air quality monitoring station data stream that the app can scrape. Data is pulled in from the selected station and the AR window loads. The AR tree grows in real-time 3D. Its L-Systems form is determined by the selected live air quality data. We used this prototype as the basis of a two-part formative participatory design workshop with 63 participants.

In the past decades, static code analysis has become a prevalent means to detect bugs and security vulnerabilities in software systems. As software becomes more complex, analysis tools also report lists of increasingly complex warnings that developers need to address on a daily basis. The novel insight we present in this work is that static analysis tools and video games both require users to take on repetitive and challenging tasks. Importantly, though, while good video games manage to keep players engaged, static analysis tools are notorious for their lacking user experience, which prevents developers from using them to their full potential, frequently resulting in dissatisfaction and even tool abandonment. We show parallels between gaming and using static analysis tools, and advocate that the user-experience issues of analysis tools can be addressed by looking at the analysis tooling system as a whole, and by integrating gaming elements that keep users engaged, such as providing immediate and clear feedback, collaborative problem solving, or motivators such as points and badges.

With the continued advancement of the internet and relevant programs, the number of exploitable loopholes in security systems increases. One such exploit that is plaguing the software scene is ransomware, a type of malware that weaves its way through these security loopholes and denies access to intellectual property and documents via encryption. The culprits will then demand a ransom as a price for data decryption. Many businesses face the issue of not having stringent security measures that are sufficient enough to negate the threat of ransomware. This jeopardizes the availability of sensitive data as corporations and individuals are at threat of losing data crucial to business or personal operations. Although certain countermeasures to deal with ransomware exist, the fact that a plethora of new ransomware cases keeps appearing every year points to the problem that they aren't effective enough. This paper aims to conceptualize practical solutions that can be used as foundations to build on in hope that more effective and proactive countermeasures to ransomware can be developed in the future.

The rapid digitalisation of the hospitality industry over recent years has brought forth many new points of attack for consideration. The hasty implementation of these systems has created a reality in which businesses are using the technical solutions, but employees have very little awareness when it comes to the threats and implications that they might present. This gap in awareness is further compounded by the existence of preestablished, often rigid, cultures that drive how hospitality businesses operate. Potential attackers are recognising this and the last two years have seen a huge increase in cyber-attacks within the sector.Attempts at addressing the increasing threats have taken the form of technical solutions such as encryption, access control, CCTV, etc. However, a high majority of security breaches can be directly attributed to human error. It is therefore necessary that measures for addressing the rising trend of cyber-attacks go beyond just providing technical solutions and make provision for educating employees about how to address the human elements of security. Inculcating security awareness amongst hospitality employees will provide a foundation upon which a culture of security can be created to promote the seamless and secured interaction of hotel users and technology.One way that the hospitality industry has tried to solve the awareness issue is through their current paper-based training. This is unengaging, expensive and presents limited ways to deploy, monitor and evaluate the impact and effectiveness of the content. This leads to cycles of constant training, making it very hard to initiate awareness, particularly within those on minimum waged, short-term job roles.This paper presents a structured approach for eliciting industry requirement for developing and implementing an immersive Cyber Security Awareness learning platform. It used a series of over 40 interviews and threat analysis of the hospitality industry to identify the requirements fo- designing and implementing cyber security program which encourage engagement through a cycle of reward and recognition. In particular, the need for the use of gamification elements to provide an engaging but gentle way of educating those with little or no desire to learn was identified and implemented. Also presented is a method for guiding and monitoring the impact of their employee's progress through the learning management system whilst monitoring the levels of engagement and positive impact the training is having on the business.

CAPTCHA is a type of challenge-response test to ensure that the response is only generated by humans and not by computerized robots. CAPTCHA are getting harder as because usage of latest advanced pattern recognition and machine learning algorithms are capable of solving simpler CAPTCHA. However, some enhancement procedures make the CAPTCHAs too difficult to be recognized by the human. This paper resolves the problem by next generation human-friendly mini game-CAPTCHA for quantifying the usability of CAPTCHAs.

The cold start problem in recommender systems refers to the inability of making reliable recommendations if a critical mass of items has not yet been rated. To bypass this problem existing research focused on developing more reliable prediction models for situations in which only few items ratings exist. However, most of these approaches depend on adjusting the algorithm that determines a recommendation. We present a complimentary approach that does not require any adjustments to the recommendation algorithm. We draw on motivation theory and reward users for rating items. In particular, we instantiate different gamification patterns and examine their effect on the average user’s number of provided report ratings. Our results confirm the positive effect of instantiating gamification patterns on the number of received report ratings.

DeepQA is a large-scale natural language processing (NLP) question-and-answer system that responds across a breadth of structured and unstructured data, from hundreds of analytics that are combined with over 50 models, trained through machine learning. After the 2011 historic milestone of defeating the two best human players in the Jeopardy! game show, the technology behind IBM Watson, DeepQA, is undergoing gamification into real-world business problems. Gamifying a business domain for Watson is a composite of functional, content, and training adaptation for nongame play. During domain gamification for medical, financial, government, or any other business, each system change affects the machine-learning process. As opposed to the original Watson Jeopardy!, whose class distribution of positive-to-negative labels is 1:100, in adaptation the computed training instances, question-and-answer pairs transformed into true-false labels, result in a very low positive-to-negative ratio of 1:100 000. Such initial extreme class imbalance during domain gamification poses a big challenge for the Watson machine-learning pipelines. The combination of ingested corpus sets, question-and-answer pairs, configuration settings, and NLP algorithms contribute toward the challenging data state. We propose several data engineering techniques, such as answer key vetting and expansion, source ingestion, oversampling classes, and question set modifications to increase the computed true labels. In addition, algorithm engineering, such as an implementation of the Newton-Raphson logistic regression with a regularization term, relaxes the constraints of class imbalance during training adaptation. We conclude by empirically demonstrating that data and algorithm engineering are complementary and indispensable to overcome the challenges in this first Watson gamification for real-world business problems.